Pairwise discrimination is known as a technique for recognizing handwritten characters. In general, this approach employs one or more special procedures to separate a pair of characters that might be readily confused. Pairwise discrimination algorithms have evolved generally from perceptual studies. For example, studies of the methods employed by human subjects to distinguish between character pairs has resulted in a theory of character discrimination based upon functional attributes. Examples of such studies are reported in the following journal articles: Y. Watanabe, J. Gyoba, T. Hirata, and K. Maruyama, "A Psychological Approach to the Human Recognition of Ambiguous Characters", J. Inst. TV Engrs. of Japan, Vol. 39, pp. 509-515, 1985; and Y. Watanabe, J. Gyoba, and K. Mauryama, "Reaction Time and Eye Movements in the Recognition Task of Hand-written Kataklna-Letters: An Experimental Verification of the Discriminant Analysis of Letter Recognition by Hayashi's Quantification", Japanese J. Psychology, Vol. 54, pp. 58-51 1983. Some pair distinction methods related to handwritten characters have been described by T. Sakai, K. Odaka. and T. Toida, "Several Approaches to Development of On-Line Handwritten Character Input Equipment", Proc. 7th Int. Conf. Pattern Recognition, pp. 1052-1054, 1984 and by C. Y. Suen and R. J. Shillman, "Low Error Rate Optical Character Recognition of Unconstrained Handprinted Letters Based on a Model of Human Perception", IEEE Trans. Systems, Man, and Cybernetics, Vol. 7, pp. 491-495, June 1977. In this latter journal article a linear discriminant is reported to be employed for differentiating between the U-V character pair and weighting coefficients are determined according to the relative importance of features determined during a learning stage. B. A. Blesser, T. T. Kuklinski and R. J. Shillman describe in a journal article entitled "Empirical Tests for Feature Selection Based On A Psychological Theory of Character Recognition", Pattern Recognition, Vol. 8, pp. 77-85, 1976 a theory of characters based on notions of physical attributes, perceptual attributes and functional attributes. A "goodness rating" was obtained from human subjects viewing a stimulus character and, for each stimulus, two mean goodness ratings were obtained by averaging across subjects (pp. 80-81).
In a journal article entitled "Automatic Recognition of Isolated Arabic Characters", Signal Processing, Vol. 14, 2 March 1988, T. El-Sheikh et al. describe a technique for pairwise discrimination that relies on a linear discriminant function. However, it is known that there are cases in which a linear discriminant function cannot separate two classes of characters. This is particularly true of cases in which the two classes are composed of similar characters. Another problem associated with the use of such a linear discriminant function is that both characters of the pair must have the same representation. This is reported to be achieved by the use of the first n coefficients of a Fourier series representation of the contour of a shape.
In an article "Cluster Analysis of English Text", IEEE, 1978, G. Toussaint et al. discuss the distribution of characters, pairs of characters and so on. The term pair-wise is employed to describe the difference between pairs of ten types of text.
In U.S. Pat. No. 3,868,635, issued Feb. 25, 1975, Shah et al. describe a system that uses dedicated hardware to recognize characters and discuss a method to count the presence and absence of character features. For example, when given an unknown character which could be either "U" or "V" the system of Shah et al. counts the number of features of the unknown character which belong to the class "U" and those features that belong to class "V". Based on these counts a decision is made. However, as stated at Col. 3, lines 11-14 Shah et al. offer no suggestion as to the type of feature that would be used. As such, it appears that their disclosure is directed not towards handwritten character recognition systems but instead towards optical character recognition systems in which fonts are generally well defined.
In a journal article entitled "On a Method of Selection of Representative Patterns Using Binary Distance Matrix" IEEE Transactions on Systems, Man, and Cybernetics, Vol. FMC-10, No. 8, August 1980, A. Som et al. propose a noniterative method of selecting representative patterns. This approach is based solely on the Euclidean distance between samples. However, the concept of Euclidean distance between certain pairs of characters can be shown to have little or no meaning. For example, a stroke by stroke comparison of the character pair "T" and "+", when written with two strokes, shows no difference. The difference between the two characters is not obtained with a Euclidean distance function but instead with a topological relationship of the two strokes. That is, for this character pair a feature "Height of Horizontal Stroke above Baseline" discriminates between the two characters.
In U.S. Pat. No. 3,111,646, issued Nov. 19, 1963, L. Harmon describes method and apparatus for measuring cursive script. Harmon apparently assumes that a comparator always arrives at a clear decision as to the identity of a written character. However, as is well known this is often not the case.
It is thus an object of the invention to provide a handwritten character recognition system that automatically selects one or more pairwise discriminant measures from a predetermined inventory of discriminants.
It is another object of the invention to provide a handwritten character recognition system that considers psychologically significant character features such as the degree of closure in a pattern or the straightness of a stroke when developing a predetermined inventory of discriminants.
It is another object of the invention to provide a handwritten character recognition system that employs pairwise discrimination measures and that determines a goodness value associated with a proposed discriminant measure as the difference of means divided by the average of standard deviations as determined from a set of training characters.
It is further object of the invention to provide an improved handwritten character recognition system that employs pairwise discrimination measures and that uses a weighted output of feature selectors in making a final decision as to an identity of a character.